Chief Data Officer: The True Dean of Big Data?

There’s a new sheriff in the big data storymap world (see Figure 1) and that’s the Chief Data Officer (CDO). While the CDO was just an idea just a few years ago, more and more organizations are either contemplating hiring or actually hiring a CDO. That is most excellent!

Figure 1: Big Data Storymap

I recently had a conversation with an organization that was looking to create a CDO role with a supporting organization. As I have stated in the past, a strong CDO candidate needs to have a background in economics. The CDO doesn’t need an IT background (that’s the CIO’s job). I recommend an economics education because economists have been trained to assign value to abstract concepts and assets. An economist is “an expert who studies the relationship between an organization’s resources and its production or output (value).” And in today’s world, assigning value to complex data sets can be extremely abstract.

A more accurate title for this role might be CDMO – Chief Data Monetization Officer – as their role needs to be focused on deriving value from, or monetizing, the organization’s data assets. This also needs to include determining how much to invest to acquire additional data sources that would complement the organization’s existing data sources and enhance their analytic results.

So let me share with you the questions that the client posed, and my answers –not that I’m convinced that my answers are correct. Thinking through these questions helps determine how valuable the data – and analytics – will be to the organization.

Question #1: How does the CDO role fit into an organization, regarding the CDO’s responsibilities?

The CDO owns quantifying the value of data and championing efforts to monetize that data. The CDO must collaborate with Lines of Business (LOB) management to determine the cost, value, and Return on Investment (ROI), for data and data-related projects.

The CDO must sit between the LOBs and the CIO (who is responsible for all IT decisions) in order to identify, value and prioritize data acquisition and monetization projects based upon both the strategic value to the organization as well as projected ROI. As a result, the CDO does not report to the CIO but likely reports to the COO or CEO (so as to have equal clout as LOB management and the CIO in leading data monetization efforts).

Question #2: What domains should fall under the CDOs role, assuming that the CDO reports to the CEO?

The Data Science team needs to fall under the purview of the CDO. The data science team is going to need a senior management champion and the CDO is the best choice. Heck, I’d even put the Business Intelligence (BI) team under the purview of the CDO in order to drive closer collaboration and share learnings across the BI and data science organizations.

But importantly, I would have the Data Scientists (and BI teams) hardline to the line of business (LOB) and dotted line into the CDO’s Data & Analytics Center of Excellence (COE). In order to drive data monetization success, it is critical that the BI and data science teams thoroughly understand the organization’s key business initiatives, the decisions that need to be made and the questions that the business users need to answer in order to provide the proper amount of analytic support. The BI and data science teams need to be accountable to the line of business first and foremost. That’s where value is being created.

By the way, the CDO probably does NOT own the data lake, the data warehouse or the underlying data architecture or technologies. These data architectures and technologies need to be owned by the CIO. Consequently, the CDO must collaborate with the CIO to create a data architecture reference architecture and data technology roadmap.

Question #3: Is a Center of Excellence approach valid for a CDO area?

Definitely! I think a Data & Analytics CoE is critical to the success of the CDO and the data monetization efforts. Key responsibilities of the CoE would include:

Hiring, development, promotion, retention and talent management of the data science and Business Intelligence teams (even if they do sit within the business units)

Continuous training program and certification on new technologies and analytic algorithms

Active industry and university monitoring to stay on top of most current data and data science trends

Capturing, sharing and management (library function) of Business Intelligence, data warehousing and advanced analytics/data science best practices across the organization

Identifying analytic processes worthy of patent protection

The CoE becomes the sun around which the data science and business intelligence personnel “orbit” from a skills and career development perspective.

What does the CDO need to do to overcome challenges of the CDO role and the organization?

The CDO will need to learn to work closely with Finance in order to develop data acquisition and data monetization ROI estimates. Finance will keep the CDO honest with respect to creating value, but expect that relationship to be a bit “challenged” because Finance seems to struggle with putting value on intangible assets like data and analytic insight. That’s where the CDO’s economics background will help.

Also, the CDO is going to have to become the master of facilitation (by the way, this is a good skill for anyone trying to bridge the gap between IT and the business). While the CDO is going to need to leverage teamwork and collaboration to be successful in their job, they are also going to have to be in the front of many of these discussions and taking the initiative – the challenge – to lead the organization and the culture to become more data and analytics tolerant.

The CDO Challenge

The CDO role will be a significant challenge. Not only is it new, so there are no predefined best practices to leverage, but also trying to determine the value of data and analytics is something at which few organizations have mastered.

The CDO will have to constantly prove his/her self to the organization. The CDO will have to champion that data and analytics can complement that decision making capabilities of management and lines of business; empowering front-line employees to be more effective in whatever key business initiatives that the organization has ordained, and create new monetization opportunities with how the organization leverages data and analytics to drive a more compelling, more relevant, more profitable customer relationship.